Since the 1960s, researchers have found various cross-sectional characteristics that predict returns in the market. These anomalies provide additional information to the market which could serve as a valuable supplement to other forms of analysis. Engelberg, McLean, and Pontiff examine whether analyst price targets and recommendations account for the information these anomalies provide. Using a collection of 125 anomalies documented in the literature, the authors find that, while anomalies tend to predict returns, analysts tend to forecast the opposite of the anomalies.
For each stock, the twelve month price target is estimated with the median target of each analyst covering the stock. The mean recommendation is also calculated where a value of 1 represents a strong sell and 5 a strong buy. The data has a mean return forecast of 36% with a 56% standard deviation while the mean recommendation is 3.77 with a standard deviation of 0.67. The analyst data spans about twenty years, up through 2017.
Each month, long and short portfolios are formed for each of the anomalies. The index Net is then created for each stock which represents the number of long portfolios minus the number of short portfolios which the stock appears in. Following Jegadeesh et al. (2004), about 30 of the anomalies are also sorted based on whether they are related to Momentum or Contrarian strategies.
Table 2 presents the main results of monthly portfolios sorted on Net, momentum, and contrarian anomalies. The returns of the Net portfolios are monotonically increasing with 18.1% annually for the long portfolio and 9.3% for the short. The analyst return forecast, however, predicts the exact opposite of this. Forecasted returns are 46.4% for the short portfolio but 32.2% for the long portfolio. Both these spreads are statistically significant and the difference between them, return forecast error, is also significant. The spread in analyst recommendation in column Rec. is consistent with the analyst forecasts but not statistically or economically significant.
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| Table 2 |
Table 2 also presents results for anomalies sorted on momentum and contrarian characteristics. As with Net, the forecast returns contradict the realized return and the difference is statistically significant. Unlike Net, both momentum and contrarian anomalies present significant spreads in analyst recommendations, but in different directions. For contrarian anomalies the recommendation spread is consistent with forecasted returns and contradicts the anomaly portfolios. However, for momentum anomalies the analyst recommendations are in agreement with the anomalies. It is noticeable that analyst recommendations for the momentum anomalies are positively correlated with realized returns while the return forecasts are negatively correlated with realized returns.
The authors further subdivide the set of analysts and stocks into categories such as stocks with recent analyst coverage increase, “All-Star Analysts” and analysts not associated with investment banking business. For all subsamples, forecast returns are negatively and significantly correlated with realized returns.
They also regress forecast error on the anomaly variables. The results show that a stock with Net of 10 (10 more long than short anomalies) has a 20% lower forecast error compared to a stock with Net -10. This confirms that return predictions for anomaly shorts are extremely high.
Finally, the authors find that analysts’ revisions to return forecasts tend to more correctly, although not fully, reflect the information from anomalies. For a firm with Net 10, the return forecast increases by 0.56% the next month compared to an average 0.09% monthly increase. A regression finds that analysts continue to update their forecasts to incorporate anomaly information up to eighteen months out.
This paper finds that, while anomalies accurately predict returns, analyst return forecasts run contrary to the anomaly predictions. Analysts predict extremely high returns for anomaly shorts while predicting lower returns for anomaly longs; a difference which is statistically significant. The literature documents that anomalies may be caused by investors’ biased expectations. The results of this paper indicate that analysts are susceptible to these same biases and overlook publicly available anomaly information.
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